Hierarchical Fractional Quantized Kernel Least mean Square Filter in Wireless Sensor Network for Data Aggregation

Nels, S. Ninisha; Singh, J. Amar Pratap

doi:10.1007/s11277-021-08509-w

Cited by 8 publications

(11 citation statements)

References 34 publications

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“…Past studies and research concentrated on data prediction models and techniques for each SN stage, CH stage as well as BS stage in which high memory, data processing, and delay control methods are required. As a result, concerning future directions, there is still a need to emphasize data prediction models to reduce the cost of redundant data transmission and data delay 12,13,59,62,63,67,76,87,90,93,109–117,121,136 …”

Section: Future Research Directionsmentioning

confidence: 99%

“…As a result, more research is required to fine‐tune the precise amount of redundant data transmissions. Further research is still recommended despite the improvements in data accuracy 41,66,67,83–84,87,90–93,111–113,115–117,121 …”

Section: Future Research Directionsmentioning

confidence: 99%

“…There are usually three stages of data transmission in WSN: In the first stage, data transmit from SN to their respective CHs within a cluster; In the second stage the data is transmitted from CHs to the BS; In the third stage, the data transmitted from BS to CH or SN for data reconstruction. Future work focuses on redundant data transmission reduction techniques to reduce data transmission at three levels, as proposed by References 13,89,111,112,115–117,121.…”

Section: Future Research Directionsmentioning

confidence: 99%

“…Energy‐efficient redundant data transmission reduction is required for scaling such WSN applications. As a result, achieving a longer network lifetime is still considered the most difficult issue in the research 12,13,57–65,67,79,83–94,109–112,114–117,121 …”

Section: Future Research Directionsmentioning

confidence: 99%

“…Second, neighboring SNs sense the same and spatially correlated data due to random deployment and close proximity of SNs. In References 39,60,62,84,87,89,92,113,114,117, data redundancy is focused, exclusively for redundant data transmissions, energy depletion, packet loss, data accuracy, WSN lifespan. As a result, there is a need to develop effective energy‐efficient data prediction methods and improve the overall network's lifespan.…”

Section: Future Research Directionsmentioning

confidence: 99%

See 4 more Smart Citations

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Jain

Kumar

Singh

2022

Trans Emerging Tel Tech

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Wireless sensor networks (WSNs) have now become a substantial part of our life as it provides an incredible setup for monitoring environmental conditions such as glaciers melting, earthquakes, climate change, volcanic eruption, agriculture, and other natural calamities. One of the most important challenges in data gathering and examining accurate data is "energy consumption." However, continuously changing environmental conditions, mobility in devices and extreme resource usage, such as battery capacity in WSNs collectively contribute to a high possibility of redundant data transmission thus making it tremendously hard to achieve a satisfactory network lifetime. Additionally, these environmental monitoring applications periodically senses and aggregate data which usually exhibits high temporal and spatial redundancies. As a result, exploiting these redundancies is essential for both resource utilization, as well as accurate data transmission. Since a huge amount of energy is exhausted in transmitting the redundant data, which has become a bottleneck in scaling such applications. In this context, this article provides a thorough examination of existing energy-efficient redundant data transmission reduction techniques, as well as their strengths and drawbacks. The idea of redundant data transmission reduction concept is divided into three stages: the sensor nodes (SN) at the first stage, the cluster heads (CHs) at the second stage, and the base station (BS) at the third stage. Furthermore, this survey focuses on the current issues and challenges, as well as the future directions in reducing redundant data transmission for future investigation.

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Section: Future Research Directionsmentioning

confidence: 99%

Section: Future Research Directionsmentioning

confidence: 99%

Section: Future Research Directionsmentioning

confidence: 99%

Section: Future Research Directionsmentioning

confidence: 99%

Section: Future Research Directionsmentioning

confidence: 99%

See 3 more Smart Citations

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Jain

Kumar

Singh

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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A two‐vectordata‐predictionmodel forenergy‐efficient data‐aggregationin wireless sensor network

Jain

Singh²

2022

Concurrency and Computation

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Most ecological management applications use wireless sensor networks (WSNs) to collect data regularly, with great temporal redundancy. As a result, a significant amount of energy is used transmitting redundant data, making it tremendously problematic to attain a satisfactory network lifetime, which is a bottleneck in enduring such environmental monitoring applications. A two‐vector data prediction model that is based on normalized quantile regression (NQR) is proposed to proficiently accomplish energy reduction in synchronous data collecting cycles. The introduced NQR algorithm provides high‐accuracy data prediction. With accurate estimates and reduced data transmission, energy usage is reduced. Furthermore, it extends the network's lifetime. In intracluster transmissions, NQR uses a two‐vector data‐prediction algorithm to coordinate the estimated sensor's reading, and, as a result, it will minimize cumulative inefficiencies from uninterrupted predictions. NQR algorithm can be integrated with both homogeneous and heterogeneous WSNs. When compared to state‐of‐art methods, the suggested NQR methodology is shown to have high energy efficiency, greater prediction accuracy, and more positive predictions with high data quality, which help the network to last longer.

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CH selection and compressive sensing‐based data aggregation in WSN using hybrid Golden circle‐inspired optimization

T P,

Srinadh,

et al. 2023

Int J Communication

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SummaryThe arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS‐based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network‐long short term memory (CNN‐LSTM) is employed for the data aggregation process. The proposed HGCIO‐based CNN‐LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.

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Hierarchical Fractional Quantized Kernel Least mean Square Filter in Wireless Sensor Network for Data Aggregation

Cited by 8 publications

References 34 publications

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

A two‐vectordata‐predictionmodel forenergy‐efficient data‐aggregationin wireless sensor network

CH selection and compressive sensing‐based data aggregation in WSN using hybrid Golden circle‐inspired optimization

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